Apache Hive is database/data warehouse software that supports data querying and analysis of large datasets stored in the Hadoop distributed file system (HDFS) and other compatible systems, and is distributed under an open source license.
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Dataloader.io
Score 10.0 out of 10
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Dataloader.io delivers a cloud based solution to import and export information from Salesforce.
Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.
Replacing data. If we've put something in a category or a bucket that is no longer named that anymore because we've evolved with the times and we want to rebrand everything, it makes it way easier to do a quick import with the new terms.
Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
Relatively easy to set up and start using.
Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.
Extracting Salesforce attachments in original file format! I do not know of a tool that can do this better, or more efficiently! This is a huge benefit to companies that would like to extract attachments from Salesforce for tasks like data migrations.
Cross-object data extract within one file. You can pull data from related objects as long as there is a populated lookup from the object you are extracting, to another object (Child or Parent).
UI is simple and requires very little to no training. Given the acquisition of Mulesoft by Salesforce, I would not be surprised if DataLoader.IO is rolled out as the new global data loading tool for Salesforce.
At the moment, I can't find a way to rename jobs. This would be useful to organize what was previously created hastily by techs in a rush.
A preview of the job, especially upserts, would take a great deal of stress away from some of us (especially those who are not so confident in their ETL practice).
A native vlookup equivalent may be a welcome addition.
It is easy to use and doesn't require a security token, so I enjoy using it. It also doesn't require any download or installation, which is sometimes a blocker to gettingthings done if the company has limits. also, the dataloader.io is easy for other people to pick up, so others can have visibility into the data jobs that have occurred
Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.
Dataloader definitely skews towards a more technical userbase. Users should be adept at manipulating data in spreadsheets and decipher JSON formatted error messaging. Additionally, there is a good amount of time need to set up the environment to map to the pertinent fields we are trying to adjust. While I would not recommend the typical account manager to use Dataloader, a typical operations manager should have no issue.
Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.
The utility itself is very self-explanatory and has enough information to guide you through the process. It has an intuitive experience for those familiar with data loading/exporting utilities. Outside of this, they have a Zendesk help center to log support requests and provide documentation to help guide you troubleshoot any issues that may be occurring.
Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed
I have used salesforce inspector also for operations like import and export of data from custom objects but it doesn't work well when you have data in huge numbers. Instead of using Salesforce Inspector, one should go for Dataloader.io if the number of records is huge to be dealt with.
HUGE time saving. When we need to clean or review data, we used to have to do it line by line. This can do the work within excel and make cleanup/management an afternoons work as opposed to a week.
Rollback what you did/change/deleted is relatively simple if you remember to back up the data you are manipulating.
